{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "c09ada11-d3a6-4837-b4d0-9657106a54b5",
   "metadata": {},
   "source": [
    "# RLSS2023 - DQN Tutorial: Fitted Q Iteration (FQI)\n",
    "\n",
    "Website: https://rlsummerschool.com/\n",
    "\n",
    "Github repository: https://github.com/araffin/rlss23-dqn-tutorial\n",
    "\n",
    "Gymnasium documentation: https://gymnasium.farama.org/\n",
    "\n",
    "## Introduction\n",
    "\n",
    "In this notebook, you will implement the Fitted Q Iteration( (FQI) algorithm to solve the [CartPole](https://gymnasium.farama.org/environments/classic_control/cart_pole/) problem.\n",
    "\n",
    "This notebooks will first cover the basics for using the Gymnasium library: how to instantiate an environment, step into it and collect training data from the FQI algorithm.\n",
    "\n",
    "You will then learn how to implement step-by-step the FQI algorithm which is the predecessor of the [Deep Q-Network (DQN)](https://stable-baselines3.readthedocs.io/en/master/modules/dqn.html) algorithm."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7fe1a9aa-5735-4614-9a76-031656397899",
   "metadata": {},
   "outputs": [],
   "source": [
    "# for autoformatting\n",
    "%load_ext jupyter_black"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8188798b-daf5-43a7-91ec-a7a922bc2034",
   "metadata": {},
   "source": [
    "## Install Dependencies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0b55494c-fff2-4459-87e1-e7399afd56d5",
   "metadata": {},
   "outputs": [],
   "source": [
    "!pip install git+https://github.com/araffin/rlss23-dqn-tutorial/ --upgrade"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fae52974-fda2-487a-9f64-80bf58125785",
   "metadata": {},
   "outputs": [],
   "source": [
    "!apt-get install ffmpeg  # For visualization"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e2369b55-266c-4dbe-b14d-250ef7386407",
   "metadata": {},
   "source": [
    "## First steps with the Gym interface\n",
    "\n",
    "An environment that follows the [gym interface](https://gymnasium.farama.org/) is quite simple to use.\n",
    "It provides to this user mainly three methods, which have the following signature (for gym versions > 0.26):\n",
    "\n",
    "- `reset()` called at the beginning of an episode, it returns an observation and a dictionary with additional info (defaults to an empty dict)\n",
    "- `step(action)` called to take an action with the environment, it returns the next observation, the immediate reward, whether new state is a terminal state (episode is finished), whether the max number of timesteps is reached (episode is artificially finished), and additional information\n",
    "- (Optional) `render()` which allow to visualize the agent in action. Note that graphical interface does not work on google colab, so we cannot use it directly (we have to rely on `render_mode='rbg_array'` to retrieve an image of the scene).\n",
    "\n",
    "Under the hood, it also contains two useful properties:\n",
    "- `observation_space` which one of the gym spaces (`Discrete`, `Box`, ...) and describe the type and shape of the observation\n",
    "- `action_space` which is also a gym space object that describes the action space, so the type of action that can be taken\n",
    "\n",
    "The best way to learn about [gym spaces](https://gymnasium.farama.org/api/spaces/) is to look at the [source code](https://github.com/Farama-Foundation/Gymnasium/tree/main/gymnasium/spaces), but you need to know at least the main ones:\n",
    "- `gym.spaces.Box`: A (possibly unbounded) box in $R^n$. Specifically, a Box represents the Cartesian product of n closed intervals. Each interval has the form of one of [a, b], (-oo, b], [a, oo), or (-oo, oo). Example: A 1D-Vector or an image observation can be described with the Box space.\n",
    "```python\n",
    "# Example for using image as input:\n",
    "observation_space = spaces.Box(low=0, high=255, shape=(HEIGHT, WIDTH, N_CHANNELS), dtype=np.uint8)\n",
    "```                                       \n",
    "\n",
    "- `gym.spaces.Discrete`: A discrete space in $\\{ 0, 1, \\dots, n-1 \\}$\n",
    "  Example: if you have two actions (\"left\" and \"right\") you can represent your action space using `Discrete(2)`, the first action will be 0 and the second 1."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "af049724-3db9-4dec-a40c-3aa025735f00",
   "metadata": {},
   "source": [
    "## CartPole Environment\n",
    "\n",
    "For this example, we will use CartPole environment, a classic control problem.\n",
    "\n",
    "\"A pole is attached by an un-actuated joint to a cart, which moves along a frictionless track. The system is controlled by applying a force of +1 or -1 to the cart. The pendulum starts upright, and the goal is to prevent it from falling over. A reward of +1 is provided for every timestep that the pole remains upright. \"\n",
    "\n",
    "Cartpole environment: [https://gymnasium.farama.org/environments/classic_control/cart_pole/](https://gymnasium.farama.org/environments/classic_control/cart_pole/)\n",
    "\n",
    "![Cartpole](https://cdn-images-1.medium.com/max/1143/1*h4WTQNVIsvMXJTCpXm_TAw.gif)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "60ec422f-2edd-40df-8da2-c1093e1da38c",
   "metadata": {},
   "outputs": [],
   "source": [
    "import gymnasium as gym\n",
    "\n",
    "# Instantiate the environment\n",
    "env = gym.make(\"CartPole-v1\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "476f88b6-4fe2-45ec-bddd-4d83feb3a86f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Box(4,) means that it is a Vector with 4 components\n",
    "print(\"Observation space:\", env.observation_space)\n",
    "print(\"Shape:\", env.observation_space.shape)\n",
    "# Discrete(2) means that there is two discrete actions\n",
    "print(\"Action space:\", env.action_space)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e414990e-2cbd-4f6f-b268-42f16a9b253b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# The reset method is called at the beginning of an episode\n",
    "obs, info = env.reset()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e89a95c8-d64a-43a3-adee-344891bbd1b7",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Sample a random action\n",
    "action = env.action_space.sample()\n",
    "print(f\"Sampled action: {action}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "311777a2-9518-496a-b1e8-0eb1af81fb7d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# step in the environment\n",
    "obs, reward, terminated, truncated, info = env.step(action)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5102a9c8-8407-4f88-b58d-4e3e5a00af3b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Note the obs is a numpy array\n",
    "# info is an empty dict for now but can contain any debugging info\n",
    "# reward is a scalar\n",
    "print(obs.shape, reward, terminated, truncated, info)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "90fbec50-1da0-4bc4-8b88-79fa38480bfe",
   "metadata": {},
   "source": [
    "### Exercise (10 minutes): write the function to collect data\n",
    "\n",
    "This function collects an offline dataset of transitions that will be used to train a model using the FQI algorithm.\n",
    "\n",
    "See docstring of the function for what is expected as input/output."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1f1b3ff1-911b-4582-91f7-49420380eda3",
   "metadata": {},
   "outputs": [],
   "source": [
    "from dataclasses import dataclass\n",
    "\n",
    "import numpy as np\n",
    "from gymnasium import spaces\n",
    "\n",
    "\n",
    "@dataclass\n",
    "class OfflineData:\n",
    "    \"\"\"\n",
    "    A class to store transitions.\n",
    "    \"\"\"\n",
    "\n",
    "    observations: np.ndarray  # same as \"state\" in the theory\n",
    "    next_observations: np.ndarray\n",
    "    actions: np.ndarray\n",
    "    rewards: np.ndarray\n",
    "    terminateds: np.ndarray"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f9f753ed-5f5d-4133-ab82-c84b3cfe2eef",
   "metadata": {},
   "outputs": [],
   "source": [
    "def collect_data(env_id: str, n_steps: int = 50_000) -> OfflineData:\n",
    "    \"\"\"\n",
    "    Collect transitions using a random agent (sample action randomly).\n",
    "\n",
    "    :param env_id: The name of the environment.\n",
    "    :param n_steps: Number of steps to perform in the env.\n",
    "    :return: The collected transitions.\n",
    "    \"\"\"\n",
    "    # Create the Gym env\n",
    "    env = gym.make(env_id)\n",
    "\n",
    "    assert isinstance(env.observation_space, spaces.Box)\n",
    "    # Numpy arrays (buffers) to collect the data\n",
    "    observations = np.zeros((n_steps, *env.observation_space.shape))\n",
    "    next_observations = np.zeros((n_steps, *env.observation_space.shape))\n",
    "    # Discrete actions\n",
    "    actions = np.zeros((n_steps, 1))\n",
    "    rewards = np.zeros((n_steps,))\n",
    "    terminateds = np.zeros((n_steps,))\n",
    "\n",
    "    # Variable to know if the episode is over (done = terminated or truncated)\n",
    "    done = False\n",
    "    # Start the first episode\n",
    "    obs, _ = env.reset()\n",
    "\n",
    "    ### YOUR CODE HERE\n",
    "    # You need to collect transitions for `n_steps` using\n",
    "    # a random agent (sample action uniformly).\n",
    "    # Do not forget to reset the environment if the current episode is over\n",
    "    # (done = terminated or truncated)\n",
    "    #\n",
    "    # TODO:\n",
    "    # 1. Sample a random action\n",
    "    # 2. Step in the env using this random action\n",
    "    # 3. Retrieve the new transition data (observation, reward, ...)\n",
    "    #  and update the numpy arrays (buffers)\n",
    "    # 4. Repeat until you collected `n_steps` transitions\n",
    "\n",
    "    for idx in range(n_steps):\n",
    "        # Sample a random action\n",
    "        action = env.action_space.sample()\n",
    "        # Step in the environment\n",
    "        next_obs, reward, terminated, truncated, info_ = env.step(action)\n",
    "\n",
    "        # Store the transition\n",
    "        # Note: we only record true termination (timeouts/truncations are artificial terminations)\n",
    "        observations[idx, :] = obs\n",
    "        next_observations[idx, :] = next_obs\n",
    "        actions[idx, :] = action\n",
    "        rewards[idx] = reward\n",
    "        terminateds[idx] = terminated\n",
    "        # Update current observation\n",
    "        obs = next_obs\n",
    "        # Check if the episode is over\n",
    "        done = terminated or truncated\n",
    "\n",
    "        # Don't forget to reset the env at the end of an episode\n",
    "        if done:\n",
    "            obs, _ = env.reset()\n",
    "\n",
    "    ### END OF YOUR CODE\n",
    "\n",
    "    return OfflineData(\n",
    "        observations,\n",
    "        next_observations,\n",
    "        actions,\n",
    "        rewards,\n",
    "        terminateds,\n",
    "    )"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dbe5d4b3-d3d6-4f82-9337-a8566c1fc707",
   "metadata": {},
   "source": [
    "Let's try the collect data method:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f499a940-a35f-4b8c-86bb-94231c41a87e",
   "metadata": {},
   "outputs": [],
   "source": [
    "env_id = \"CartPole-v1\"\n",
    "n_steps = 50_000\n",
    "# Collect transitions for n_steps\n",
    "data = collect_data(env_id=env_id, n_steps=n_steps)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8ae0713b-63f3-40f0-8471-f3f7e3c53f88",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Check the length of the collected data\n",
    "assert len(data.observations) == n_steps\n",
    "assert len(data.actions) == n_steps\n",
    "# Check that there are multiple episodes in the data\n",
    "assert not np.all(data.terminateds)\n",
    "assert np.any(data.terminateds)\n",
    "# Check the shape of the collected data\n",
    "if env_id == \"CartPole-v1\":\n",
    "    assert data.observations.shape == (n_steps, 4)\n",
    "    assert data.next_observations.shape == (n_steps, 4)\n",
    "assert data.actions.shape == (n_steps, 1)\n",
    "assert data.rewards.shape == (n_steps,)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f815ff42-3c8e-4ab5-835f-70438171751b",
   "metadata": {},
   "outputs": [],
   "source": [
    "from pathlib import Path\n",
    "\n",
    "from dqn_tutorial.fqi import save_data\n",
    "\n",
    "output_filename = Path(\"../data\") / f\"{env_id}_data\"\n",
    "# Create folder if it doesn't exist\n",
    "output_filename.parent.mkdir(parents=True, exist_ok=True)\n",
    "\n",
    "# Save collected data using numpy\n",
    "save_data(data, output_filename)"
   ]
  },
  {
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"
    }
   },
   "cell_type": "markdown",
   "id": "2ff52b1a-6eb1-4284-b25f-8d59ab005ed5",
   "metadata": {},
   "source": [
    "## Fitted Q Iteration (FQI) Algorithm\n",
    "\n",
    "\n",
    "<div>\n",
    "    <img src=\"attachment:008df18a-8225-4766-aa89-c713f5f953af.png\" width=\"500\"/>\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "045b251a-9ace-450b-afa9-7ee4128cdef6",
   "metadata": {},
   "outputs": [],
   "source": [
    "from functools import partial\n",
    "from pathlib import Path\n",
    "from typing import Optional\n",
    "\n",
    "import gymnasium as gym\n",
    "import numpy as np\n",
    "from gymnasium import spaces\n",
    "from sklearn import tree\n",
    "from sklearn.base import RegressorMixin\n",
    "from sklearn.exceptions import NotFittedError\n",
    "from sklearn.ensemble import GradientBoostingRegressor, RandomForestRegressor\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.preprocessing import PolynomialFeatures\n",
    "from sklearn.neighbors import KNeighborsRegressor"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "239ad0de-bec9-4918-a691-4322d062c45a",
   "metadata": {},
   "source": [
    "### Choosing a Model\n",
    "\n",
    "With FQI, you can use any regression model.\n",
    "\n",
    "Here we are choosing a [k-nearest neighbors regressor](https://scikit-learn.org/stable/modules/neighbors.html#regression), but one could choose a linear model, a decision tree, a neural network, ..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1d8d873e-bc40-4a35-b98b-4fe9ce916aab",
   "metadata": {},
   "outputs": [],
   "source": [
    "# First choose the regressor\n",
    "model_class = partial(KNeighborsRegressor, n_neighbors=30)  # LinearRegression, GradientBoostingRegressor"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "be7a24b4-a416-4607-af3a-9dfdbc203fc1",
   "metadata": {},
   "source": [
    "### Loading offline dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1fe29bd2-ea95-4778-b466-537a088615b0",
   "metadata": {},
   "outputs": [],
   "source": [
    "from dqn_tutorial.fqi import load_data\n",
    "\n",
    "env_id = \"CartPole-v1\"\n",
    "output_filename = Path(\"../data\") / f\"{env_id}_data.npz\"\n",
    "render_mode = \"rgb_array\"\n",
    "\n",
    "# Create test environment\n",
    "env = gym.make(env_id, render_mode=render_mode)\n",
    "\n",
    "# Load saved transitions\n",
    "data = load_data(output_filename)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a7c0f461-9c04-48f2-ad57-21b555a7c041",
   "metadata": {},
   "source": [
    "### First Iteration\n",
    "\n",
    "For $n = 0$, the initial training set is defined as:\n",
    "\n",
    "- $x = (s_t, a_t)$\n",
    "- $y = r_t$\n",
    "\n",
    "We fit a regression model $f_\\theta(x) = y$ to obtain $ Q^{n=0}_\\theta(s, a) $"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "26940b3e-4c9a-4632-a198-2e0ce7b12b88",
   "metadata": {},
   "outputs": [],
   "source": [
    "# First iteration:\n",
    "# The target q-value is the reward obtained\n",
    "targets = data.rewards.copy()\n",
    "# Create input for current observations and actions\n",
    "# Concatenate the observations and actions\n",
    "# so we can predict qf(s_t, a_t)\n",
    "current_obs_input = np.concatenate((data.observations, data.actions), axis=1)\n",
    "# Fit the estimator for the current target\n",
    "model = model_class().fit(current_obs_input, targets)"
   ]
  },
  {
   "attachments": {
    "e3d00cba-8345-4c65-8027-ab06d99d2305.png": {
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"
    }
   },
   "cell_type": "markdown",
   "id": "5bf9544c-87f4-4872-9ae9-eef49c3a040a",
   "metadata": {},
   "source": [
    "### 1. Exercise (10 minutes): write the function to predict Q-Values\n",
    "\n",
    "\n",
    "<div>\n",
    "    <img src=\"attachment:e3d00cba-8345-4c65-8027-ab06d99d2305.png\" width=\"300\"/>\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1d1c52d1-4336-4caa-9d91-cfb641e7972a",
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_q_values(\n",
    "    model: RegressorMixin,\n",
    "    obs: np.ndarray,\n",
    "    n_actions: int,\n",
    ") -> np.ndarray:\n",
    "    \"\"\"\n",
    "    Retrieve the q-values for a set of observations.\n",
    "    qf(q_t, action) for all possible actions.\n",
    "\n",
    "    :param model: Q-value estimator\n",
    "    :param obs: A batch of observations\n",
    "    :param n_actions: Number of discrete actions.\n",
    "    :return: The predicted q-values for the given observations\n",
    "        (batch_size, n_actions)\n",
    "    \"\"\"\n",
    "    batch_size = len(obs)\n",
    "    q_values = np.zeros((batch_size, n_actions))\n",
    "\n",
    "    ### YOUR CODE HERE\n",
    "    # TODO: for every possible actions a:\n",
    "    # 1. Create the regression model input $(s, a)$ for the action a\n",
    "    # and states s (here a batch of observations)\n",
    "    # 2. Predict the q-values for the batch of states\n",
    "    # 3. Update q-values array for the current action a\n",
    "\n",
    "    # Predict q-value for each action\n",
    "    for action_idx in range(n_actions):\n",
    "        # Note: we should do one hot encoding if not using CartPole (n_actions > 2)\n",
    "        # Create a vector of size (batch_size, 1) for the current action\n",
    "        # This allows to do batch prediction for all the provided observations\n",
    "        actions = action_idx * np.ones((batch_size, 1))\n",
    "        # Concatenate the observations and the actions to obtain\n",
    "        # the input to the q-value estimator\n",
    "        # you can use `np.concatenate()`\n",
    "        model_input = np.concatenate((obs, actions), axis=1)\n",
    "        # Predict q-values for the given observation/action combination\n",
    "        # shape: (batch_size, 1)\n",
    "        predicted_q_values = model.predict(model_input)\n",
    "        # Update the q-values array for the current action\n",
    "        q_values[:, action_idx] = predicted_q_values\n",
    "\n",
    "    ### END OF YOUR CODE\n",
    "\n",
    "    return q_values"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "353de669-1744-43be-a3dc-5310d23c72a4",
   "metadata": {},
   "source": [
    "Let's test it with a subset of the collected data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6910968b-16db-41fe-a6d9-ff8c65322c85",
   "metadata": {},
   "outputs": [],
   "source": [
    "n_observations = 2\n",
    "n_actions = int(env.action_space.n)\n",
    "\n",
    "q_values = get_q_values(model, data.observations[:n_observations], n_actions)\n",
    "\n",
    "assert q_values.shape == (n_observations, n_actions)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a883ff38-48e9-4f7c-8692-33a9d1b325d2",
   "metadata": {},
   "source": [
    "### 2. Exercise (5 minutes): write the function to evaluate a model\n",
    "\n",
    "A greedy policy $\\pi(s)$ can be defined using the q-value:\n",
    "\n",
    "$\\pi(s) = argmax_{a \\in A} Q(s, a)$.\n",
    "\n",
    "It is the policy that take the action with the highest q-value for a given state."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2b1f4b18-a811-43fa-925d-20ad2f4d52d7",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "\n",
    "from gymnasium.wrappers import RecordVideo\n",
    "\n",
    "\n",
    "def evaluate(\n",
    "    model: RegressorMixin,\n",
    "    env: gym.Env,\n",
    "    n_eval_episodes: int = 10,\n",
    "    video_name: Optional[str] = None,\n",
    ") -> None:\n",
    "    episode_returns, episode_reward = [], 0.0\n",
    "    total_episodes = 0\n",
    "    done = False\n",
    "\n",
    "    # Setup video recorder\n",
    "    video_recorder = None\n",
    "    if video_name is not None and env.render_mode == \"rgb_array\":\n",
    "        os.makedirs(\"../logs/videos/\", exist_ok=True)\n",
    "\n",
    "        # New gym recorder always wants to cut video into episodes,\n",
    "        # set video length big enough but not to inf (will cut into episodes)\n",
    "        env = RecordVideo(env, \"../logs/videos\", step_trigger=lambda _: False, video_length=100_000)\n",
    "        env.start_recording(video_name)\n",
    "\n",
    "    obs, _ = env.reset()\n",
    "    n_actions = int(env.action_space.n)\n",
    "    assert isinstance(env.action_space, spaces.Discrete), \"FQI only support discrete actions\"\n",
    "\n",
    "    while total_episodes < n_eval_episodes:\n",
    "\n",
    "        ### YOUR CODE HERE\n",
    "\n",
    "        # Retrieve the q-values for the current observation\n",
    "        # you need to re-use `get_q_values()`\n",
    "        # Note: you need to add a batch dimension to the observation\n",
    "        # you can use `obs[np.newaxis, ...]` for that: (obs_dim,) -> (batch_size=1, obs_dim)\n",
    "        q_values = get_q_values(\n",
    "            model,\n",
    "            obs[np.newaxis, ...],\n",
    "            n_actions,\n",
    "        )\n",
    "        # Select the action that maximizes the q-value for each state\n",
    "        # Don't forget to remove the batch dimension, you can `.item()` for that\n",
    "        best_action = int(np.argmax(q_values, axis=1).item())\n",
    "\n",
    "        # Send the action to the env\n",
    "        obs, reward, terminated, truncated, _ = env.step(best_action)\n",
    "\n",
    "        ### END OF YOUR CODE\n",
    "\n",
    "        episode_reward += float(reward)\n",
    "\n",
    "        done = terminated or truncated\n",
    "        if done:\n",
    "            episode_returns.append(episode_reward)\n",
    "            episode_reward = 0.0\n",
    "            total_episodes += 1\n",
    "            obs, _ = env.reset()\n",
    "\n",
    "    if isinstance(env, RecordVideo):\n",
    "        print(f\"Saving video to ../logs/videos/{video_name}\")\n",
    "        env.close()\n",
    "\n",
    "    print(f\"Total reward = {np.mean(episode_returns):.2f} +/- {np.std(episode_returns):.2f}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "23e326d9-1fa7-4efd-a841-b1d9e5ef9484",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Evaluate the first iteration\n",
    "evaluate(model, env, n_eval_episodes=10)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eced782e-653d-4041-8473-2c39d12ba9a9",
   "metadata": {},
   "source": [
    "### 3. Exercise (15 minutes): the fitted Q iterations\n",
    "\n",
    "1. Create the training set based on the previous iteration $ Q^{n-1}_\\theta(s, a) $ and the transitions:\n",
    "- input: $x = (s_t, a_t)$\n",
    "- if $s_{t+1}$ is non-terminal: $y = r_t + \\gamma \\cdot \\max_{a' \\in A}(Q^{n-1}_\\theta(s_{t+1}, a'))$\n",
    "- if $s_{t+1}$ is terminal, do not bootstrap: $y = r_t$\n",
    "\n",
    "2. Fit a model $f_\\theta$ using a regression algorithm to obtain $ Q^{n}_\\theta(s, a)$\n",
    " \n",
    "\\begin{aligned}\n",
    " f_\\theta(x) = y\n",
    "\\end{aligned}\n",
    "\n",
    "4. Repeat, $n = n + 1$"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5d824b98-6360-4d4e-a3e2-584c0c36665e",
   "metadata": {},
   "source": [
    "First, let's define some constants:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7cbff0d8-eab0-4cec-847e-23bc1c509359",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Max number of iterations\n",
    "n_iterations = 20\n",
    "# How often do we evaluate the learned model\n",
    "eval_freq = 2\n",
    "# How many episodes to evaluate every eval-freq\n",
    "n_eval_episodes = 10\n",
    "# discount factor\n",
    "gamma = 0.99\n",
    "# Number of discrete actions\n",
    "n_actions = int(env.action_space.n)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "18d564da-7170-4bf8-8259-f19d0b12bcbd",
   "metadata": {},
   "source": [
    "Then do several iteration of the FQI algorithm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "21c99207-4a80-4263-8b13-1818f65632e1",
   "metadata": {},
   "outputs": [],
   "source": [
    "for iter_idx in range(n_iterations):\n",
    "    ### YOUR CODE HERE\n",
    "    # TODO:\n",
    "    # 1. Compute the q values for the next states using\n",
    "    # the previous regression model\n",
    "    # 2. Keep only the next q values that correspond\n",
    "    # to the greedy-policy\n",
    "    # 3. Construct the regression target (TD(0) target)\n",
    "    # 4. Fit a new regression model with this new target\n",
    "\n",
    "    # Construct TD(0) target\n",
    "    # using current model and the next observations\n",
    "    \n",
    "    # First, retrieve the q-values for the next states\n",
    "    # for all possible actions\n",
    "    # you need to use `get_q_values()` method\n",
    "    next_q_values = get_q_values(\n",
    "        model,\n",
    "        data.next_observations,\n",
    "        n_actions=n_actions,\n",
    "    )\n",
    "    # Follow-greedy policy: use the action with the highest q-value\n",
    "    # to compute the next q-values\n",
    "    next_q_values = next_q_values.max(axis=1)\n",
    "    # The new target is the reward + what our agent expect to get\n",
    "    # if it follows a greedy policy (follow action with the highest q-value)\n",
    "    # Reminder: you should not bootstrap if terminated=True\n",
    "    # (you can mask the next q values for that using `np.logical_not`)\n",
    "    should_bootstrap = np.logical_not(data.terminateds) # (1 - data.terminateds)\n",
    "    targets = data.rewards + gamma * should_bootstrap * next_q_values\n",
    "    # Update the q-value estimate with the current target,\n",
    "    # i.e., fit a regression model using the new target\n",
    "    model = model_class().fit(current_obs_input, targets)\n",
    "\n",
    "    ### END OF YOUR CODE\n",
    "\n",
    "    if (iter_idx + 1) % eval_freq == 0:\n",
    "        print(f\"Iter {iter_idx + 1}\")\n",
    "        print(f\"Score: {model.score(current_obs_input, targets):.2f}\")\n",
    "        evaluate(model, env, n_eval_episodes)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e3477740-bfe4-4e0b-8a5d-cd0206786c8b",
   "metadata": {},
   "source": [
    "### Record a video of the trained agent"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "da8d90cc-2d77-4682-a97c-a3dacd081f38",
   "metadata": {},
   "outputs": [],
   "source": [
    "eval_env = gym.make(env_id, render_mode=\"rgb_array\")\n",
    "video_name = f\"FQI_{env_id}\"\n",
    "n_eval_episodes = 3\n",
    "\n",
    "evaluate(model, eval_env, n_eval_episodes, video_name=video_name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bfc66910-de41-4622-9811-96addf8ba8d3",
   "metadata": {},
   "outputs": [],
   "source": [
    "from dqn_tutorial.notebook_utils import show_videos\n",
    "\n",
    "print(f\"FQI agent on {env_id} after {n_iterations} iterations:\")\n",
    "show_videos(\"../logs/videos/\", prefix=video_name)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "69a60230-6477-4318-8d60-10f6eada6064",
   "metadata": {},
   "source": [
    "### Going further\n",
    "\n",
    "- play with different models, and with their hyperparameters\n",
    "- play with the discount factor\n",
    "- play with the number of data collected/used\n",
    "- combine data from random policy with data from trained model"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "40b8a44f-d48c-4a9d-8ed4-968c1ffb9948",
   "metadata": {},
   "source": [
    "## Conclusion\n",
    "\n",
    "What we have seen in this notebook:\n",
    "- collecting data using a random agent in a gym environment\n",
    "- predicting q-values using a regression model\n",
    "- the fitted q-iteration (FQI) algorithm to learn from an offline dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "761010b8-86dd-4958-b097-2912606b4493",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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